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Clinical data mining to predict patient diagnoses

The sheer volume of healthcare data is growing at an astronomical rate. The importance clinical data mining has never been greater.

The healthcare industry is among the most information-intensive industries out there. Medical information, knowledge, and clinical data mining keep growing on a daily basis. The sheer volume of healthcare data is growing at an astronomical rate. It’s estimated that 153 exabytes (one exabyte = one billion gigabytes) were produced in 2013 and an estimated 2,314 exabytes will be produced in 2020, translating to an overall rate of increase of at least 48% annually.

Data analytics is arguably the most significant revolution in healthcare over the last decade. Clinical data mining holds great potential for the healthcare industry to enable health systems to systematically and efficiently use data analytics to identify and analyse patient diagnoses well in advance.

For more than a decade, healthcare organisations invested millions of dollars into building data warehouses and armies of data analysts, with the sole purpose of making better decisions with data while improving patient outcomes.

The historical problem has been that these warehouses and analytics alone aren’t enough. They just tell you what’s happening, but they cannot explain why it’s happening and what one can do to instigate the right outcomes. Now, instead of just understanding “what’s going on”, the infrastructure and technology have now evolved to a point that they can now figure out “why” and “what to do about it”.

Read on to find out some of the areas that have been blessed by this revolutionary advancement in the healthcare space – clinical data mining.

Identifying patient diagnosis well in advance
Big data applications are driving a revolution in healthcare because they quickly transform mountains of unused data into quickly available, actionable intelligence, which improves decision-making tremendously.

A clinical study has found that 4 – 17% of patients undergo cardiopulmonary or
respiratory arrest while in the hospital. Early detection and intervention are essential to preventing these serious, often life-threatening events. Indeed, early detection and treatment of patients with sepsis have already shown promising results, resulting in significantly lower mortality rates. This approach could be implemented across the health space with clinical data mining proving to be a huge asset to the medical community.

Doctors and patients are able to take faster and necessary action

The growth being witnessed in medical data computing power and technical functionality is enabling the medical field to reinvent itself in ways never before possible. Medicine has made tremendous strides over the past century, but with the influx of big data, some observers believe the field of medicine is prepared to push the bounds even further to improve healthcare faster. Data mining on medical data has great potential to improve the treatment quality of hospitals and increase the survival rate of patients.

Today, NGOs, companies, and other major philanthropic organisations are investing not just in breakthrough experimental research but also in clinical data mining, as there is a strong belief that this technological advancement is paving the way for the future. No longer will we have to wait for aeons for the doctor to get back with us. With new predictive models and methods used and expert data mining, we are able to get results almost instantaneously.

Everybody can benefit from clinical data mining – not just doctors and patients

Healthcare providers and insurance companies can both enjoy a piece of the data mining cake.

Healthcare providers use data mining and data analysis to find the best practices and the most effective treatments. These tools compare symptoms, causes, treatments, and negative effects and then proceed to analyse which action will prove most effective for a group of patients. This is also a way for providers to develop the best standards of care and clinical best practices.

On the other side of things, insurers are now able to better detect medical insurance abuse and fraud because of data mining. Unusual claims patterns are easier to spot with this tool and it can identify inappropriate referrals and fraudulent medical and insurance claims. When insurers reduce their losses due to fraud, the cost of healthcare also decreases. As a result, healthcare facilities and groups can use data mining tools to reach better patient-related decisions.

Key takeaways

Data mining is used to uncover patterns from a large amount of stored information and then used to build predictive models. Since the early 90s, this practice has been used to help with fraud detection, credit scoring, and maintenance scheduling but it’s finally being utilised in healthcare programs across the world.

We can all agree that clinical data mining is the way of the future and is highly beneficial to all key stakeholders in the health space and beyond – especially, patients.

For more information on clinical data mining and the software that enable such powerful analyses, check out Selerity.